{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 一.分析背景\n",
    "数据集是2014年11月18日到12月18日共1个月的电商用户行为数据.数据字段说明如下.通过对这些数据进行统计、分析，从中发现用户使用产品的规律，并将这些规律与网站的营销策略、产品功能、运营策略相结合，发现营销、产品和运营中可能存在的问题，解决这些问题就能优化用户体验、实现更精细和精准的运营与营销，让产品获得更好的增长。\n",
    "\n",
    "| 字段          | 解释                                        |\n",
    "| ------------- | ------------------------------------------- |\n",
    "| user_id       | 用户身份                                    |\n",
    "| item_id       | 商品ID                                      |\n",
    "| behavior_type | 1代表点击 2代表收藏 3代表加购物车 4代表支付 |\n",
    "| user_geohash  | 地理位置                                    |\n",
    "| item_category | 品类ID（商品所属的品类）                    |\n",
    "| time          | 用户行为发生的时间                          |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 二.结论先行\n",
    "基于电商分析的'人-货-场'模型:\n",
    "![image-20210224001044957](https://cdn.jsdelivr.net/gh/Wi2077/image/image-20210224001044957.png) \n",
    "\n",
    "分析结论如下:\n",
    "\n",
    "1.流量分析:\n",
    "\n",
    "   - 网站pv和uv平时比较稳定,双12活动促销效果良好.\n",
    "   - 21:00和22:00是用户一天中最活跃的时候,平台开展各种活动应首选这个时间段进行.\n",
    "\n",
    "2.转化分析:\n",
    "\n",
    "**商品点击→收藏/添加购物车**的转化率(约7.5%)和**商品点击→购买**的转化率(平时为1.5%,双12为3%)较低.商品销售转化流程需要优化.可从以下方面进行:\n",
    "- 优化搜索引擎，利用用户画像优化商品匹配，个性化地推荐用户感兴趣的商品\n",
    "- 优化商品界面加购与收藏按键布局，以便用户触达\n",
    "\n",
    "3.留存分析:\n",
    "\n",
    "   - 用户留存率很高,双12活动更明显提升了用户留存.\n",
    "\n",
    "4.商品销售情况分析:\n",
    "\n",
    "   - 有88.43%的商品销售量等于1件,体现了电商销售的长尾效应.但是20%的热门商品品类贡献了约82%的销量.\n",
    "\n",
    "   - 商品点击量最高的两个商品的销售量都很低,应进一步排查原因.\n",
    "\n",
    "5.用户分组,比较分析其商品购买偏好的差异:\n",
    "\n",
    "   - 高购买频率用户群和低购买频率用户群的商品购买偏好有明显差异,需要做进一步分析,以优化对不同用户群的商品推荐机制.\n",
    "\n",
    "6.用RFM模型对用户进行分类后，可知重要挽留客户占比最高。建议根据用户类型，进行有针对性的精准营销。\n",
    "\n",
    "<font color=\"blue\">**具体分析过程如下:**</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 三.数据预处理\n",
    "导入数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>behavior_type</th>\n",
       "      <th>user_geohash</th>\n",
       "      <th>item_category</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>98047837</td>\n",
       "      <td>232431562</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4245</td>\n",
       "      <td>2014-12-06 02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>97726136</td>\n",
       "      <td>383583590</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5894</td>\n",
       "      <td>2014-12-09 20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>98607707</td>\n",
       "      <td>64749712</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2883</td>\n",
       "      <td>2014-12-18 11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>98662432</td>\n",
       "      <td>320593836</td>\n",
       "      <td>1</td>\n",
       "      <td>96nn52n</td>\n",
       "      <td>6562</td>\n",
       "      <td>2014-12-06 10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>98145908</td>\n",
       "      <td>290208520</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13926</td>\n",
       "      <td>2014-12-16 21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    user_id    item_id  behavior_type user_geohash  item_category  \\\n",
       "0  98047837  232431562              1          NaN           4245   \n",
       "1  97726136  383583590              1          NaN           5894   \n",
       "2  98607707   64749712              1          NaN           2883   \n",
       "3  98662432  320593836              1      96nn52n           6562   \n",
       "4  98145908  290208520              1          NaN          13926   \n",
       "\n",
       "            time  \n",
       "0  2014-12-06 02  \n",
       "1  2014-12-09 20  \n",
       "2  2014-12-18 11  \n",
       "3  2014-12-06 10  \n",
       "4  2014-12-16 21  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "df = pd.read_csv('UserBehaviorData.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "检查数据发现只有user_geohash列有空数据."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id          0.00000\n",
       "item_id          0.00000\n",
       "behavior_type    0.00000\n",
       "user_geohash     0.68001\n",
       "item_category    0.00000\n",
       "time             0.00000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()/df.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "去除重复数据(完全相同的两行数据算作重复数据)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "去除重复数据之前数据有12256906行\n",
      "去除重复数据之后数据有8164040行\n"
     ]
    }
   ],
   "source": [
    "print('去除重复数据之前数据有{}行'.format(df.shape[0]))\n",
    "df=df.drop_duplicates()\n",
    "print('去除重复数据之后数据有{}行'.format(df.shape[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "格式化behavior_type列的数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "behavior = {1:'pv', 2:'fav', 3:'cart', 4:'buy'}\n",
    "df['Behavior_type'] = df['behavior_type'].map(lambda x: behavior[x])    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "格式化时间数据并生成Date和Hour,以便后续处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数据时间范围是从2014-11-18 00:00:00到2014-12-18 23:00:00\n"
     ]
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "\n",
    "df['Time'] = df['time'].map(lambda x: datetime.strptime(str(x), '%Y-%m-%d %H'))\n",
    "df['date'] = df['Time'].map(lambda x: x.date())\n",
    "df['hour'] = df['Time'].map(lambda x: x.hour)\n",
    "print('数据时间范围是从{}到{}'.format(df.Time.min(), df.Time.max()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如下所示,数据集是10000名用户在一个月内的电商行为数据."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id            10000\n",
       "item_id          2876947\n",
       "behavior_type          4\n",
       "user_geohash      575458\n",
       "item_category       8916\n",
       "time                 744\n",
       "Behavior_type          4\n",
       "Time                 744\n",
       "date                  31\n",
       "hour                  24\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 四.数据分析\n",
    "### 1.用户流量分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pv = df[df['Behavior_type']=='pv'].groupby(['date']).size()\n",
    "uv = df[['user_id','date']].drop_duplicates().groupby(['date']).size()\n",
    "\n",
    "# 画图\n",
    "fig, ax1 = plt.subplots(figsize = (9, 6))\n",
    "sns.set_style('ticks')\n",
    "# 左轴\n",
    "ax1.plot(pv, '-o', color='r', label='pv')\n",
    "ax1.set_xlabel('Date')\n",
    "ax1.set_ylabel('pv')\n",
    "ax1.legend(loc='upper left', fontsize=14)\n",
    "# 右轴\n",
    "ax2 = ax1.twinx()\n",
    "ax2.plot(uv, '-o',color='b', label='uv')\n",
    "ax2.set_ylabel('uv')\n",
    "ax2.legend(loc='upper right', fontsize=14)\n",
    "\n",
    "#处理x轴的日期标签\n",
    "fig.autofmt_xdate()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看到,12/10到12/16的PV和UV数据均明显高于12/10之前(12/12当天迎来峰值),从吸引流量的角度来说,此次双12活动效果良好."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x20c3f14e2e0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#分析比较一天不同时间内用户的活跃情况(分成平时和双12)\n",
    "pv_by_hour = df[df['date']<datetime.strptime('14-11-29', '%y-%m-%d').date()].groupby(['hour']).size()/11\n",
    "pv_by_hour1 = df[df['date']==datetime.strptime('14-12-12', '%y-%m-%d').date()].groupby(['hour']).size()\n",
    "plt.figure(figsize=(9, 6))\n",
    "sns.set_style('ticks')\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "plt.plot(pv_by_hour, '-o', label='非促销日')\n",
    "plt.plot(pv_by_hour1, '-o',label='双12促销日')\n",
    "plt.legend(loc='upper left', fontsize=12) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看到,21:00和22:00是用户最活跃的时候,用户最喜欢在晚上这个时间段浏览商品，所以商家应集中在这个时段进行一些促销活动."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.电商销售转化分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fav = df[df['Behavior_type']=='fav'].groupby(['date']).size()\n",
    "cart = df[df['Behavior_type']=='cart'].groupby(['date']).size()\n",
    "buy = df[df['Behavior_type']=='buy'].groupby(['date']).size()\n",
    "\n",
    "fig, ax1 = plt.subplots(figsize = (9, 6))\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "plt.plot(fav, '-o', label='收藏')\n",
    "plt.plot(cart, '-o',label='添加购物车')\n",
    "plt.plot(buy, '-o',label='购买')\n",
    "plt.legend(loc='upper left', fontsize=12) \n",
    "\n",
    "fig.autofmt_xdate()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "收藏,添加购物车和购买数也都在双12当天达到了峰值.接下来分析转化率:\n",
    "(将转化过程简化为:点击→收藏/添加购物车→购买)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cr_pv2fav = (fav + cart)/pv  #点击→收藏/添加购物车的转化率\n",
    "#cr_fav2buy = buy/(fav + cart) #收藏/添加购物车→购买的转化率\n",
    "cr_pv2buy = buy/pv  #点击→购买的转化率\n",
    "\n",
    "fig, ax1 = plt.subplots(figsize = (9, 6))\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "plt.plot(cr_pv2fav, '-o', label='点击→收藏/添加购物车的转化率')\n",
    "#plt.plot(cr_fav2buy, '-o',label='收藏/添加购物车→购买')\n",
    "plt.plot(cr_pv2buy, '-o',label='点击→购买的转化率')\n",
    "plt.legend(loc='best', fontsize=12)\n",
    "fig.autofmt_xdate()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 12/13到12/18期间,商品点击→收藏/添加购物车的转化率明显下降,这是因为12/13到12/18期间的商品浏览量仍明显高于平时,而收藏和添加购物车量却迅速跌至和平时相当的水平,12/12之后的巨大流量并未得到有效的转化.\n",
    "- **商品点击→收藏/添加购物车**的转化率(约7.5%)和**商品点击→购买**的转化率(平时为1.5%,双12为3%)较低.商品销售转化流程需要优化.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 用户购买行为分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "复购率：产生两次或两次以上购买的用户占购买用户的比例为91.45%\n"
     ]
    }
   ],
   "source": [
    "user_buy = df[df['Behavior_type']=='buy'].groupby(['user_id']).size()\n",
    "print('复购率：产生两次或两次以上购买的用户占购买用户的比例为{:.2f}%'.format(len(user_buy[user_buy>1])/len(user_buy)*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.0, 300.0)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAicAAAFzCAYAAADlp6mhAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO3df1TV9eHH8dcFRCWQPAXUIZtlidV11srsODp2PPQDOG11qzEhK/w1k5aZkYw0mm2uXytKM7PSimZddKR4YhtjTTGcWkxXTmHnmBBZyvzBTbwgXO79/sGXO4GrF/Cib+j5+Gfd9/31ue/zWTx7fz73cy0ej8cjAAAAQwSd7Q0AAAA4EXECAACMQpwAAACjECcAAMAoxAkAADAKcQL0MU1NTWd7E7xM2hYA/QdxAvQhJSUlmjNnTree88033+iNN97ole3JyMhQWVlZwF7vvffe0/Hjx3XfffepsrJSb775purr6/Xkk0/q008/Ddj7tGloaFBLS0uXHut2u7V169aAbwOAzkLO9gYA3zf19fW64YYbNHLkSEnSoUOH5Ha7FRUVJUnat2+fcnJylJSU1Om5N954o9588001NDRo8ODBne4fN26c93Vramr09ttvq6ysTM3NzT635Q9/+IPWrVun4OBgn/c7nU41NTWpqKhItbW1mj59usLCwiRJFotFR44c0YIFCxQTEyOp9Q+40+lUbm6uRowYIUmqqKhQamqqhg0b1u61v/32Wy1evFjjxo3zjrlcLi1dulQhISFqaGhQUVGRpk2bpi1btmj69Ok+t/GBBx7QsWPHNGjQoHbjjY2NioiI0IoVK7xj6enpqq+vV0hI67/6qqurNWjQIO/2u1wuRUVFaenSpZ3ep6GhQY8++qhee+01/fCHP/S5LXfeeadcLpcGDBjg835J2r9/vzZv3nzS+wEQJ8AZFxoaqujoaBUUFEiS3nrrLR0/flyzZs2SJGVlZXn/eErSb37zG33yySftXuPOO+/0/vOIESP06quvSpIiIyOVm5urpqYm/e53v1NwcLDWrVsnh8Ohv/zlL5JaA+Ldd9/V0KFDlZaWprS0NH388ce64YYbFBYWpi1btqimpkb33HOPlixZorq6OlksFsXExKiwsFCSNH/+fN1444269dZbJUkPPfSQpk6dqmuuuabT5z3nnHN0zTXX6K233mo3npWV1e6P+PHjxxUdHS2LxSKHw6Ft27bpuuuu06effiqn0+mNm6amJoWGhrabz2PHjvmc6xPnUZJWrlzZ7vbUqVP18MMPa8yYMZ2e63a75XK5vO91zjnnKCsrq9N7uVwuBQUFKSgoSCEhIVq8eLEuuugi/fOf/9TSpUv1xhtvyGKxeB87ceJEn9sK4H+IE+As+O9//6tJkyZJkg4cOCCPx6NNmzZJav2v+YSEBO9jDx48qF//+tftVhjabN26Vc8//7z3tsVi0caNG1VRUSFJKi8v14UXXqj8/HzvY5KSkhQU1P6I7tdff6177rlHzz33nP7zn//oq6++ktR6SOjnP/95p/edPn26Fi1apFtvvVX19fX64osvdNVVV/n8rKe6CPWJ9zU3N+uzzz7T559/rp07d2rEiBGKjIzU+++/L7fbLZvNpm+//VbnnHOO1q5dq/DwcEmS1WpVSEiIBg4c2O61jx8/LpfL1ek9165d642U6upqZWVleQPkjjvuUHp6uiTpiy++UGZmpoKCgtTY2CiHw+FdYTmR2+3W4sWLFRcX542ho0ePasGCBRo4cKDuuusu7d27V4899phSUlI6BROAzvh/CXCGBQUFKSoqSu+//74k3ysnJzrZIZcTX6+NxWJRSEiI9w/3119/rRkzZmjmzJlatmyZJKmlpaXda3744Yf6yU9+oiuvvFKNjY1qbGzURRddJElatGhRu/cqLCzUCy+8oB/84AeSpMmTJ0uSLr74Yk2dOlV79+5VTk6Obr75Zu9zXC6XDh48qJ/+9Kedtv3EFZCBAwdq2LBhOnDggAYOHKghQ4YoMjJS27dvV1pamjIyMjRnzhylp6crPDxcJSUlWrFihSIiIjqFSZvm5malpqYqOztbVqtVkvTdd98pMTFR06ZNa/fYtWvXqqqqynt7zJgxKi4uliRt3rxZdrtdL7/8ss/3OZHb7dbMmTMVGRmp6667TmPGjNHq1as1adIkud1uv88HQJwAZ1x3/0D5+q//E524+uDxeNTc3OyNkzvuuEN79uzRZZddpp07d8pqtcrlcrX7r/e6ujrdeeedWrhwoW688UatX79e8fHxPt8rNDRUt956q5544gnv6kV0dLT+9Kc/6ZFHHtFvf/vbTisDl156qdatW+f3c+7fv19Dhw5VZmamXnnlFf3yl79USUmJXC6X/v3vf0tqPU/l4osvliQlJCQoISFBTzzxhM4991yFhITo0KFDkqTzzjtPLpdLDodDr732Wrv3CQoKUn5+vjZu3Nhu/PDhw7rtttt8bpvT6dQnn3zS6f4f/OAHev311zu9/oIFCzR06FBNmjRJ77zzjl5//XUFBQURJ0AXESfAGdbU1KTa2lrZbDZJ/zshtqSkRFLrCbEnHtapq6vT448/7vME2JaWFkVHR3tvezwe1dbWek+udbvd+uMf/6gFCxYoKytLCxYs8K6ctIVMenq6xo0bp6NHj0qS9u7d610RaXtMSEhIuxWaDz/8UKtWrdLy5csVGRmpLVu26N5779Vll13WbvsmTpzoXR1xOBx67LHHdODAAa1atard6s7OnTvlcDj09ttv67vvvpPH49HMmTMVEhKiDz74QJMnT5bD4dCxY8d07rnntnuPXbt2aeDAgRowYIAOHDggSYqJiVFzc/NJv+p8xRVXdAqwHTt2+Hys1BpFU6ZMUUZGhnfss88+0+LFizs9dv/+/d7x22+/XT/60Y9kt9v15JNPasGCBSd9DwD/Q5wAZ9iBAwd09dVX67333pN06sM6LS0t2r17t9577z3vt3BOxeVyae/evRo7dqy++eYbVVRUaPPmzXr00UdVW1urv/3tb2ppaVFISIgOHz6s6dOntzskcuzYMVVVVWn+/PntXjMnJ8d7WERqDY3Q0FClp6dr8ODB+vLLL7V+/XotWbKk3fYMGDBAf/7znyVJy5cv1+DBgzVgwADNnj1b99xzjyR5T6q1Wq1atWqVJk2apNWrV2vTpk3auHGjBg0apJtvvlkZGRk+z7txuVxKSkpSeHi4tm3bJkm6/vrrVV9fr6KiIp/zdO6553oPTbWpqak56bxu3rxZU6ZMaTd28OBBbwSe6IILLtBzzz2n6OhorVu3Ti+++KJ+9rOfaeHChSc9/ASgPeIEOMP+9a9/nfTk0Y62bdumkJCQTisSJ7N69WqlpqbqyiuvVGJioi699FK98847uuKKK1RVVaXq6mpJreemnHfeed5vDEmtIfTQQw/ptttu0+jRozV+/PiTnu/ywAMPaPv27Zo/f753FeLEFZw2bd9SOVHHk3FPfExtba2sVqv3/JWHH35YknTLLbfo97//vTfg2ng8HmVlZXlXSIYMGSKpNRAkae7cuT63f9OmTdq9e3e7sbq6Ot1+++2dHvuPf/xD33zzja677rp244cOHfK+z4nbI7Wu3CxfvlzFxcW65ZZbdP311ysiIkIul+uUJwgDaEWcAGdYUVGRHnzwQe9tl8vlvRBYRUWFvvrqKw0aNEhNTU16/vnnlZ6e3ukP+sm88MILSktL0yWXXKKZM2fqgw8+8IbQpZdeqvPOO8/nH8eqqirl5ORoxIgR3m/iPPvss3rsscd00003eR934nMvvvhiLVy4UJIUFxcnSZ0uaObxeLznaTgcDs2bN09ut1svv/yy96vFbYdipNZzOJKTk/XKK6/okUceUWVlpYqLi/Xaa69p3rx5WrBggZ599llvKNTU1Cg3N9d7jZO21/ryyy8ltZ4QO2bMmE5hcO+992rGjBnttrWgoEDV1dXec3xCQkJUXV2tJ554Qrm5ud6IOnbsmBwOh/76178qOTm53WuceH7QjBkzdNddd2n9+vXau3evLr/88k6PAeAbcQKcQfv371dLS4uuvfZa79hll13m/YNVWFiouLg4XXvttaqsrFRMTIzuv//+Lr12WVmZjh07pnvvvVdBQUGaOnWq9u3b5z30kJeXpyVLlui+++7zPmf9+vX66KOP9O233+rRRx/VhAkTJEk5OTnatWuXfvWrX6moqEjPPfecpPaXq++4KrFw4ULt3Lmz3XkZzc3N+vjjjyW1XmPE5XKpubm53WGdxMRESa0XOZsxY4auuOIKLV68WFFRUVq3bp1WrVqlF198UZdccomuuuoqvfrqq3r11VfV0NCgrKwsDR061BsObReIa/tfj8ejX/ziF1q8eLE+++wzLVmyxHttlbbDTR39/e9/15QpUzRhwgRlZGRo0aJF7S66lpubq5KSEt1www3ebW/T0tKiGTNm+LwIW9u1aLp6RVrg+8ziYY0R6Dc8Ho/PQylS6x/FoKCgdvfv3LlTjY2NnQ5ZtGlqalJlZaVGjx7t973r6+u9J7n2Fx0v+ObPkSNHNGTIEL9f/wZwasQJAAAwCj/8BwAAjEKcAAAAoxAnAADAKMZ8W2fcuHGKjY0925sBAAACYN++fdq6dWuPnmtMnMTGxra7IBQAAOi72n6ioyc4rAMAAIxCnAAAAKMQJwAAwCjECQAAMApxAgAAjEKcAAAAoxAnAADAKMQJAAAwCnECAACMQpwAAACjECcAAMAoxAkAADAKcQIAAIxiTJx4PCe/r7G55cxtCAAAOKtCzvYGtLFYpOFZH/m8r+qZ5DO8NQAA4GwxZuUEAABAIk4AAIBhiBMAAGAU4gQAABiFOAEAAEYhTgAAgFGIEwAAYBTiBAAAGIU4AQAARiFOAACAUYgTAABgFOIEAAAYhTgBAABGIU4AAIBRiBMAAGAU4gQAABiFOAEAAEYhTgAAgFGIEwAAYBTiBAAAGCXE3wOOHj2qOXPmyO12a/DgwXrppZd0yy23aNiwYZKk+fPnKy4uTtnZ2dqzZ48mTJigWbNmSZLPMQAAgFPxu3JSWFio9PR0rVixQueff76WL1+u5ORk5eXlKS8vT3FxcSouLpbb7ZbdbldNTY2qqqp8jgEAAPjjd+UkLS3N+89HjhzRhRdeqA0bNmjr1q0aOXKkFi5cqG3btikxMVGSFB8fr/Lycu3evbvT2PDhw3vnUwAAgH7Db5y02b59uxwOh8aPHy+bzabo6Gg9/vjj2rhxo5xOp2JiYiRJkZGR2rdvn8+xjux2u+x2e4A+CgAA6A+6FCd1dXV6+umntXjxYkVFRSk0NFSSZLVaVV1drbCwMDU2NkqSnE6n3G63z7GOUlJSlJKSIkmy2WwB+UAAAKBv83vOSVNTk2bPnq25c+cqNjZWmZmZqqioUEtLi0pKSjRq1ChZrVaVl5dLkioqKhQbG+tzDAAAwB+/Kydr1qzRrl27tGzZMi1btkzjxo1TZmamJGnixIkaP3686uvrlZqaqtraWpWWlio/P18Wi6XTGAAAgD8Wj8fjCcQLORwOlZWVaezYsYqKijrp2MnYbDb9c+RUn/dVPZMciE0EAABniM1mU0FBQY+e2+UTYv2JjIxUUlKS3zEAAIBT4QqxAADAKMQJAAAwCnECAACMQpwAAACjECcAAMAoxAkAADAKcQIAAIxCnAAAAKMQJwAAwCjECQAAMApxAgAAjEKcAAAAoxAnAADAKMQJAAAwCnECAACMQpwAAACjECcAAMAoxAkAADAKcQIAAIxCnAAAAKMQJwAAwCjECQAAMApxAgAAjEKcAAAAoxAnAADAKMQJAAAwCnECAACMQpwAAACjECcAAMAoxAkAADAKcQIAAIxCnAAAAKMQJwAAwCjECQAAMApxAgAAjEKcAAAAoxAnAADAKMQJAAAwCnECAACMQpwAAACjECcAAMAoxAkAADAKcQIAAIxCnAAAAKMQJwAAwCjECQAAMApxAgAAjEKcAAAAo4T4e8DRo0c1Z84cud1uDR48WC+99JKeeuop7dmzRxMmTNCsWbMkSdnZ2V0aAwAAOBW/KyeFhYVKT0/XihUrdP7556uoqEhut1t2u101NTWqqqpScXFxl8YAAAD88btykpaW5v3nI0eOqLCwUPfff78kKT4+XuXl5dq9e7cSExP9jg0fPrwXPgIAAOhP/MZJm+3bt8vhcCg2NlYxMTGSpMjISO3bt09Op7NLYx3Z7XbZ7fZAfA4AANBPdOmE2Lq6Oj399NNatGiRwsLC1NjYKElyOp1yu91dHusoJSVFBQUFKigoCNTnAQAAfZzfOGlqatLs2bM1d+5cxcbGymq1qry8XJJUUVHRrTEAAAB//B7WWbNmjXbt2qVly5Zp2bJlstlsWrdunWpra1VaWqr8/HxZLBalpqb6HQMAAPDH4vF4PN19ksPhUFlZmcaOHauoqKhujZ2MzWbTP0dO9Xlf1TPJ3d1EAABwFtlsth6fttHlE2JPFBkZqaSkpB6NAQAAnEqfuEJsY3NLt8YBAEDf1aOVkzNt0IBgDc/6qNM4h3sAAOh/+sTKCQAA+P4gTgAAgFGIEwAAYBTiBAAAGIU4AQAARiFOAACAUYgTAABgFOIEAAAYhTgBAABGIU4AAIBRiBMAAGAU4gQAABiFOAEAAEYhTgAAgFGIEwAAYBTiBAAAGIU4AQAARiFOAACAUYgTAABgFOIEAAAYhTgBAABGIU4AAIBRiBMAAGAU4gQAABiFOAEAAEYhTgAAgFGIEwAAYBTiBAAAGIU4AQAARiFOAACAUYgTAABgFOIEAAAYhTgBAABGIU4AAIBRiBMAAGAU4gQAABiFOAEAAEYhTgAAgFGIEwAAYBTiBAAAGIU4AQAARiFOAACAUYgTAABgFOIEAAAYhTgBAABGIU4AAIBRiBMAAGCULsXJwYMHlZqaKklyuVy66aabNHnyZE2ePFmVlZWSpOzsbKWkpGjp0qXe5/kaAwAAOBW/ceJwODRv3jw1NDRIkiorK5WcnKy8vDzl5eUpLi5OxcXFcrvdstvtqqmpUVVVlc8xAAAAf/zGSXBwsHJzcxUeHi5J2rFjhzZs2KC7775b2dnZcrlc2rZtmxITEyVJ8fHxKi8v9znWkd1ul81mk81mC+RnAgAAfZjfOAkPD1dERIT39ujRo7Vy5UqtWbNGLpdLGzdulNPpVExMjCQpMjJShw4d8jnWUUpKigoKClRQUBCozwMAAPq4kO4+YdSoUQoNDZUkWa1WVVdXKywsTI2NjZIkp9Mpt9vtcwwAAMCfbn9bJzMzUxUVFWppaVFJSYlGjRolq9XqPWxTUVGh2NhYn2MAAAD+dHvlJCMjQ3PnzpUkTZw4UePHj1d9fb1SU1NVW1ur0tJS5efny2KxdBoDAADwp8txkpeXJ0kaOXKk1q9f3+6+8PBw5eXlqaysTNOmTfOeo+JrDAAA4FS6vXJyMpGRkUpKSvI7BgAAcCpcIRYAABiFOAEAAEYhTgAAgFGIEwAAYBTiBAAAGIU4AQAARiFOAACAUYgTAABgFOIEAAAYhTgBAABGIU4AAIBR+nScNDa39Og+AABgroD98N/ZMGhAsIZnfeTzvqpnks/w1gAAgEDo0ysnAACg/yFOAACAUYgTAABgFOIEAAAYhTgBAABGIU4AAIBRiBMAAGAU4gQAABiFOAEAAEYhTgAAgFGIEwAAYBTiBAAAGIU4AQAARiFOAACAUYgTAABgFOIEAAAYhTgBAABGIU4AAIBRiBMAAGAU4gQAABiFOAEAAEYhTgAAgFGIEwAAYBTiBAAAGIU4AQAARiFOAACAUYgTAABgFOIEAAAYhTgBAABGIU4AAIBRiBMAAGAU4gQAABiFOAEAAEYhTgAAgFGIEwAAYJQuxcnBgweVmprqvZ2dna2UlBQtXbq022MAAACn4jdOHA6H5s2bp4aGBklScXGx3G637Ha7ampqVFVV1eUxAAAAf/zGSXBwsHJzcxUeHi5J2rZtmxITEyVJ8fHxKi8v7/JYR3a7XTabTTabLWAfCAAA9G1+4yQ8PFwRERHe206nUzExMZKkyMhIHTp0qMtjHaWkpKigoEAFBQUB+TAAAKDv6/YJsWFhYWpsbJTUGiput7vLYwAAAP50O06sVqv3EE1FRYViY2O7PAYAAOBPSHefkJCQoNTUVNXW1qq0tFT5+fmyWCxdGgMAAPCnyysneXl5klrPQcnLy9OYMWP07rvvKiIiostjAAAA/nR75URqPcE1KSmpR2MAAACnwhViAQCAUYgTAABgFOIEAAAYhTgBAABGIU4AAIBRiBMAAGAU4gQAABiFOAEAAEYhTgAAgFGIEwAAYBTiBAAAGIU4AQAARiFOAACAUfptnDQ2t3RrHAAAmCHkbG9Abxk0IFjDsz7qNF71TPJZ2BoAANBV/XblBAAA9E3ECQAAMApxAgAAjEKcAAAAoxAnAADAKMQJAAAwCnECAACMQpwAAACjECcAAMAoxAkAADAKcQIAAIxCnAAAAKMQJwAAwCjECQAAMApxAgAAjEKcAAAAoxAnAADAKMQJAAAwCnECAACMQpwAAACjECcAAMAoxAkAADAKcQIAAIxCnAAAAKMQJwAAwCjECQAAMApxAgAAjEKcAAAAoxAnAADAKMQJAAAwyvcuThqbW7o1DgAAzqyQs70BZ9qgAcEanvVRp/GqZ5LPwtYAAICOvncrJwAAwGzdXjlxuVxKSEjQsGHDJEnz58/XO++8oz179mjChAmaNWuWJCk7O7vTGAAAgD/dXjmprKxUcnKy8vLylJeXp+rqarndbtntdtXU1KiqqkrFxcWdxgAAALqi2ysnO3bs0IYNG7R161aNHDlSoaGhSkxMlCTFx8ervLxcu3fv7jQ2fPjwgG44AADon7q9cjJ69GitXLlSa9askcvlUmlpqWJiYiRJkZGROnTokJxOZ6cxX+x2u2w2m2w222l8BAAA0J90O05GjRql6OhoSZLVatWRI0fU2NgoSXI6nXK73QoLC+s05ktKSooKCgpUUFDQ0+0HAAD9TLfjJDMzUxUVFWppaVFJSYlycnJUXl4uSaqoqFBsbKysVmunMQAAgK7o9jknGRkZmjt3riRp4sSJSkhIUGpqqmpra1VaWqr8/HxZLJZOYwAAAF3R7TgZOXKk1q9f324sLy9PZWVlmjZtmiIiIk46BgAA4E9ArhAbGRmppKQkv2MAAAD+cIVYAABgFOIEAAAYhTgBAABGIU4AAIBRiBMAAGAU4gQAABiFOPl/jc0tPboPAAAEVkCuc9IfDBoQrOFZH/m8r+qZ5DO8NQAAfH+xcgIAAIxCnAAAAKMQJwAAwCjECQAAMApxAgAAjEKcAAAAoxAnAADAKMQJAAAwCnECAACMQpwAAACjECcAAMAoxAkAADAKcQIAAIxCnAAAAKMQJwAAwCjECQAAMApxAgAAjEKcAAAAoxAnAADAKMRJFzQ2t3RrHAAA9FzI2d6AvmDQgGANz/qo03jVM8lnYWsAAOjfWDk5DadaOWFVBQCAnmHl5DScbEVFYlUFAICeYuUEAAAYhTjpJZxECwBAz3BYp5dwEi0AAD3DygkAADAKcQIAAIxCnAAAAKMQJwAAwCjEiUH4hg8AAHxb54xrbG7RoAHBPu/jGz4AABAnZxxXlQUA4NQ4rAMAAIxCnAAAAKMQJ30Av34MAPg+4ZyTPuBU56lUPH2bz/FTnXgLAIDJiJM+jm/4AAD6Gw7rfA9xPRUAgMlYOemnenI9lZMdIvL3egAABFKvx0l2drb27NmjCRMmaNasWb39dvh/PbmeCtdgAQCYoFcP6xQXF8vtdstut6umpkZVVVW9+XboRd09FMQhIgBAT/Xqysm2bduUmJgoSYqPj1d5ebmGDx/em2+JXnKqE28DdYjoVIeO+ttzAAAnZ/F4PJ7eevHs7Gzdd999GjVqlD755BPt2rVLM2bM8N5vt9tlt9slSXv37tUll1zSW5vyvXLkyBENHTr0bG9Gv8BcBhbzGVjMZ2Axn4G1d+9ebd++vUfP7dWVk7CwMDU2NkqSnE6n3G53u/tTUlKUkpLSm5vwvWSz2VRQUHC2N6NfYC4Di/kMLOYzsJjPwLLZbD1+bq+ec2K1WlVeXi5JqqioUGxsbG++HQAA6Ad6deUkISFBqampqq2tVWlpqfLz83vz7QAAQD8Q/NRTTz3VWy8eGhqqpKQkNTU16aGHHuJY3hlktVrP9ib0G8xlYDGfgcV8BhbzGVg9nc9ePSEWAACgu7h8PQAAMApxAgAAeqyurk5lZWU6fPhwwF6TOOnjXC6XbrrpJk2ePFmTJ09WZWWlsrOzlZKSoqVLl57tzetTDh48qNTUVO9tX/PI3HbdifPpaz+VmM+uOHr0qKZNm6YpU6YoIyNDTU1N7Junwdd8sm/2nMPh0MyZM/X555/r/vvv1+HDhwOyfxInfVxlZaWSk5OVl5envLw8VVdX85MBPeBwODRv3jw1NDRI8v3TC/wcQ9d1nM+O+2lcXBzz2UWFhYVKT0/XihUrdP7556uoqIh98zR0nM/ly5ezb56GyspKZWVl6cEHH1R8fLy2bNkSkP2TOOnjduzYoQ0bNujuu+9Wdna2Nm/e3OknA+BfcHCwcnNzFR4eLsn3Ty/4GoNvHeez437qcrmYzy5KS0vTj3/8Y0mtVzAtLCxk3zwNHeczJCSEffM0XH/99br66qv16aef6vPPP9emTZsCsn8SJ33c6NGjtXLlSq1Zs0Yul0ulpaWKiYmRJEVGRurQoUNneQv7hvDwcEVERHhvO53OTvPoawy+dZzPjvvpxo0bmc9u2r59uxwOhy644AL2zQBom8/x48ezb54mj8ejoqIiDRkyRBaLJSD7J3HSx40aNUrR0dGSWr9PfuTIkVP+ZAC6xtdPL/j7OQacXMf9tLq6mvnshrq6Oj399NNatGgR+2YAnDif7Junz2KxKCcnR3Fxcdq+fXtA9k/ipI/LzMxURUWFWlpaVFJSopycHH4yIAB8/fQCP8fQcx3301GjRjGfXdTU1KTZs2dr7ty5J90Pmcuu6zif7JunZ/ny5Vq7dq2k1pONZ8yYEZD9s1cvX4/el5GRoblz589Zm5wAAACySURBVEqSJk6cyE8GBIivebRYLMxtD3XcT8ePH6/6+nrmswvWrFmjXbt2admyZVq2bJlsNpvWrVvHvtlDHedz3LhxyszMlMS+2RMpKSl65JFHtHr1al1++eVKSEhQWlraae+fXCG2H3I4HCorK9PYsWMVFRV1tjenz/I1j8xtYDGfPcO+2fuYz54LxP5JnAAAAKNwzgkAADAKcQIAAIxCnAAAAKMQJwAAwCjECQAAMMr/AZkLj8bEKLg2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize = (9, 6))\n",
    "plt.rcParams['font.sans-serif']=['SimHei']\n",
    "#sns.histplot(data=user_buy, kde=True)\n",
    "plt.hist(user_buy, bins=200)\n",
    "plt.title('用户购买商品数量直方图')\n",
    "plt.xlim(1,300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "有6人购买商品数超过150件\n",
      "付费用户群体中,有56.95%的用户购买商品数低于10件\n"
     ]
    }
   ],
   "source": [
    "print('有{}人购买商品数超过150件'.format(sum(user_buy>150)))\n",
    "print('付费用户群体中,有{:.2f}%的用户购买商品数低于10件'.format(sum(user_buy<10)/len(user_buy)*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如下所述,有6个人购买商品数超过150件,甚至有770件,需要进一步排查是否有异常,如刷单等."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id\n",
       "33448326     189\n",
       "35306096     179\n",
       "51492142     413\n",
       "56560718     239\n",
       "122338823    770\n",
       "123842164    571\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_buy[user_buy>150]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.用户留存分析(计算次日留存率和7日留存率)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from datetime import datetime,timedelta\n",
    "def get_retention_rate(date, df, day):\n",
    "    #计算留存率\n",
    "    user0 = df[df['date']==date]['user_id'].unique()    #当天的用户id list\n",
    "    user1 = df[df['date']==date + timedelta(days=day)]['user_id'].unique()    #几天后的用户id list\n",
    "    return len(np.intersect1d(user0, user1))/len(user0)\n",
    "    \n",
    "rr1 = []\n",
    "rr7 = []\n",
    "date_list = pv.index\n",
    "for date in date_list:\n",
    "    rr1.append(get_retention_rate(date, df, 1))\n",
    "    rr7.append(get_retention_rate(date, df, 7))\n",
    "\n",
    "fig, ax1 = plt.subplots(figsize = (9, 6))\n",
    "plt.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签\n",
    "plt.plot(date_list[:30], rr1[:30], '-o', label='次日留存率')\n",
    "plt.plot(date_list[:24], rr7[:24], '-o',label='7日留存率')\n",
    "plt.legend(loc='best', fontsize=12)\n",
    "#plt.ylim(0.65, 1)\n",
    "fig.autofmt_xdate()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看到,用户的次日留存率和7日留存率都很高,为70%-80%,平台用户粘性很好."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.商品销售情况分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "有88.43%的商品销售量等于1件\n",
      "有99.89%的商品销售量小于10件\n",
      "单一商品销量最高为38件\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(1.0, 40.0)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "product_sale = df[df['Behavior_type']=='buy'].groupby(['item_id']).size()\n",
    "print('有{:.2f}%的商品销售量等于1件'.format(sum(product_sale==1)/len(product_sale)*100))\n",
    "print('有{:.2f}%的商品销售量小于10件'.format(sum(product_sale<10)/len(product_sale)*100))\n",
    "print('单一商品销量最高为{}件'.format(max(product_sale)))\n",
    "plt.subplots(figsize = (9, 6))\n",
    "plt.hist(product_sale, bins=80)\n",
    "#sns.histplot(data=product_sale, kde=True)\n",
    "plt.xlim(1, 40)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "有88.43%的商品销售量等于1件,这体现了电商销售的长尾效应.\n",
    "单一商品销量最高为38件,无商品销售爆款."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "有67.01%的商品品类销售量小于10件\n",
      "有4.59%的商品品类销售量大于100件\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '商品种类销售量直方图')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "product_kind_sale = df[df['Behavior_type']=='buy'].groupby(['item_category']).size().sort_values(ascending=False)\n",
    "print('有{:.2f}%的商品品类销售量小于10件'.format(sum(product_kind_sale<10)/len(product_kind_sale)*100))\n",
    "print('有{:.2f}%的商品品类销售量大于100件'.format(sum(product_kind_sale>100)/len(product_kind_sale)*100))\n",
    "plt.subplots(figsize = (9, 6))\n",
    "plt.rcParams['font.sans-serif']=['SimHei']\n",
    "plt.hist(product_kind_sale[product_kind_sale<100], bins=50)\n",
    "#sns.histplot(data=product_kind_sale[product_kind_sale<100], kde=True)\n",
    "plt.xlim(1, 100)\n",
    "plt.title('商品种类销售量直方图')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sale_per_product = product_kind_sale.cumsum()/product_kind_sale.sum()\n",
    "product_kind_per = np.arange(1,len(product_kind_sale)+1)/len(product_kind_sale)\n",
    "\n",
    "plt.subplots(figsize = (9, 6))\n",
    "plt.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签\n",
    "plt.plot(product_kind_per, sale_per_product.values)\n",
    "plt.xlabel('商品种类比例', fontsize=12, color='k')\n",
    "plt.ylabel('商品销售量比例', fontsize=12, color='k')\n",
    "plt.xlim(0, 1)\n",
    "plt.ylim(0, 1)\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "销量前20%的商品品类贡献了约82%的销量."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "接下来尝试找出有哪些商品是浏览量非常高但是销量却很低"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '商品销售量')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "product_click = df[df['Behavior_type']=='pv'].groupby(['item_id']).size()\n",
    "productID_click2sale = pd.DataFrame({ 'click' :product_click, 'sale' :product_sale}).fillna(0)   #?是否已经依据index自动对齐\n",
    "\n",
    "plt.subplots(figsize = (9, 6))\n",
    "plt.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签\n",
    "plt.scatter(productID_click2sale['click'], productID_click2sale['sale'])\n",
    "plt.xlabel('商品浏览量', fontsize=12, color='k')\n",
    "plt.ylabel('商品销售量', fontsize=12, color='k')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如上图,商品浏览量最高的两个商品的销售量都很低,初步排查发现其高浏览量并未集中于某几个用户或者某几天,需要结合其他数据进一步排查原因,可以考虑降低其曝光量.\n",
    "其商品id为:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "两个高点击低销售量的商品id是:[97655171, 112921337]\n"
     ]
    }
   ],
   "source": [
    "print('两个高点击低销售量的商品id是:{}'.format(product_click[product_click>700].index.to_list()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.依据购买频率对用户分组,比较他们购买商品习惯的区别\n",
    "这里暂只依据购买频率对用户进行分组,结合前面的分析结果,取购买商品数量最高的10%用户(记作组A),和购买商品数量最少的40%用户(记作组B),比较两组用户的购买商品偏好."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "user_buy.sort_values(ascending = False,inplace = True)\n",
    "user_groupA = user_buy[:int(len(user_buy)*0.1)].index   #求出组A的用户id\n",
    "user_groupB = user_buy[int(len(user_buy)*0.6):].index    #求出组A的用户id\n",
    "\n",
    "def get_user_group(x, user_groupA, user_groupB):\n",
    "    \"\"\"依据userid计算出所属组别\"\"\"\n",
    "    if x in user_groupA:\n",
    "        return 'A'\n",
    "    elif x in user_groupB:\n",
    "        return 'B'\n",
    "    else:\n",
    "        return 'NA'\n",
    "    \n",
    "df['user_group'] = df['user_id'].agg(get_user_group, args=(user_groupA, user_groupB))\n",
    "product_saleA = df[(df['Behavior_type']=='buy') & (df['user_group']=='A')].groupby(['item_category']).size().sort_values(ascending=False)\n",
    "product_saleB = df[(df['Behavior_type']=='buy') & (df['user_group']=='B')].groupby(['item_category']).size().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "组A和组B最喜欢购买的商品种类Top15及其购买数量如下:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>组A</th>\n",
       "      <th>组B</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>item_category</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1838</th>\n",
       "      <td>NaN</td>\n",
       "      <td>90.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1863</th>\n",
       "      <td>642.0</td>\n",
       "      <td>218.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2825</th>\n",
       "      <td>267.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2993</th>\n",
       "      <td>NaN</td>\n",
       "      <td>94.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3064</th>\n",
       "      <td>NaN</td>\n",
       "      <td>95.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3424</th>\n",
       "      <td>NaN</td>\n",
       "      <td>214.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3628</th>\n",
       "      <td>NaN</td>\n",
       "      <td>98.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5027</th>\n",
       "      <td>NaN</td>\n",
       "      <td>93.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5232</th>\n",
       "      <td>496.0</td>\n",
       "      <td>127.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5399</th>\n",
       "      <td>384.0</td>\n",
       "      <td>109.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5894</th>\n",
       "      <td>394.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6344</th>\n",
       "      <td>678.0</td>\n",
       "      <td>89.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6513</th>\n",
       "      <td>386.0</td>\n",
       "      <td>87.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6648</th>\n",
       "      <td>NaN</td>\n",
       "      <td>81.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6977</th>\n",
       "      <td>906.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7957</th>\n",
       "      <td>393.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8877</th>\n",
       "      <td>345.0</td>\n",
       "      <td>130.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9516</th>\n",
       "      <td>325.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10392</th>\n",
       "      <td>260.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10431</th>\n",
       "      <td>NaN</td>\n",
       "      <td>109.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10894</th>\n",
       "      <td>289.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11279</th>\n",
       "      <td>NaN</td>\n",
       "      <td>105.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11537</th>\n",
       "      <td>281.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13230</th>\n",
       "      <td>322.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  组A     组B\n",
       "item_category              \n",
       "1838             NaN   90.0\n",
       "1863           642.0  218.0\n",
       "2825           267.0    NaN\n",
       "2993             NaN   94.0\n",
       "3064             NaN   95.0\n",
       "3424             NaN  214.0\n",
       "3628             NaN   98.0\n",
       "5027             NaN   93.0\n",
       "5232           496.0  127.0\n",
       "5399           384.0  109.0\n",
       "5894           394.0    NaN\n",
       "6344           678.0   89.0\n",
       "6513           386.0   87.0\n",
       "6648             NaN   81.0\n",
       "6977           906.0    NaN\n",
       "7957           393.0    NaN\n",
       "8877           345.0  130.0\n",
       "9516           325.0    NaN\n",
       "10392          260.0    NaN\n",
       "10431            NaN  109.0\n",
       "10894          289.0    NaN\n",
       "11279            NaN  105.0\n",
       "11537          281.0    NaN\n",
       "13230          322.0    NaN"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame({ '组A' :product_saleA[:15], '组B' :product_saleB[:15]})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由上可知,组A和组B的最喜欢购买商品种类Top15只有6个相同项,两个组的购买偏好有明显差异."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.用户价值分析\n",
    "基于RFM模型进行用户价值分析,由于缺少订单金额数据,故只从R和F两个方面进行."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>F</th>\n",
       "      <th>date</th>\n",
       "      <th>R</th>\n",
       "      <th>R_score</th>\n",
       "      <th>F_score</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4913</th>\n",
       "      <td>6</td>\n",
       "      <td>2014-12-16</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>重要保持客户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6118</th>\n",
       "      <td>1</td>\n",
       "      <td>2014-12-17</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>重要保持客户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7528</th>\n",
       "      <td>6</td>\n",
       "      <td>2014-12-13</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>重要挽留客户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7591</th>\n",
       "      <td>21</td>\n",
       "      <td>2014-12-13</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>重要发展客户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12645</th>\n",
       "      <td>8</td>\n",
       "      <td>2014-12-14</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>重要挽留客户</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          F        date    R  R_score  F_score   label\n",
       "user_id                                               \n",
       "4913      6  2014-12-16  3.0      5.0      1.0  重要保持客户\n",
       "6118      1  2014-12-17  2.0      5.0      1.0  重要保持客户\n",
       "7528      6  2014-12-13  6.0      4.0      1.0  重要挽留客户\n",
       "7591     21  2014-12-13  6.0      4.0      3.0  重要发展客户\n",
       "12645     8  2014-12-14  5.0      4.0      1.0  重要挽留客户"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfm = df[df['Behavior_type']=='buy'].pivot_table(index = 'user_id',\n",
    "                         values= ['date','Behavior_type'],\n",
    "                        aggfunc={'date':'max',        #最近购买日期\n",
    "                                'Behavior_type':'count'})   #购买次数\n",
    "rfm['R'] = (datetime.strptime('14-12-19', '%y-%m-%d').date()-rfm.date)/np.timedelta64(1,'D') #以14-12-19为基准计算R\n",
    "rfm.rename(columns={'Behavior_type':'F'},inplace=True)\n",
    "rfm['R_score'] = pd.cut(rfm['R'],bins = [0,5,10,15,20,30],labels=[5,4,3,2,1],right=False).astype(float)\n",
    "rfm['F_score'] = pd.cut(rfm['F'],bins = [0,10,20,30,35,45],labels=[1,2,3,4,5],right=False).astype(float)\n",
    "\n",
    "def rfm_func(x):\n",
    "    level = x.apply(lambda x:'1' if x>=0 else '0')\n",
    "    label = level.R_score+level.F_score\n",
    "    d = {'11':'重要价值客户',\n",
    "        '10':'重要保持客户',\n",
    "        '01':'重要发展客户',\n",
    "        '00':'重要挽留客户' }\n",
    "    return d[label]\n",
    "rfm['label'] = rfm[['R_score','F_score']].apply(lambda x:x-x.mean()).apply(rfm_func,axis = 1)\n",
    "rfm.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "label\n",
       "重要价值客户    2344\n",
       "重要保持客户    2001\n",
       "重要发展客户    1173\n",
       "重要挽留客户    3368\n",
       "Name: label, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfm_count = rfm.groupby('label')['label'].count()\n",
    "rfm_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20c3f5626d0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize = (7, 7))\n",
    "rfm_count.plot.pie(autopct='%.2f%%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 对于重要价值用户，他们是最优质的用户，需要重点关注并保持， 应该提高满意度，增加留存；\n",
    "\n",
    "- 对于重要保持用户，他们最近有购买，但购买频率不高，可以通过活动等提高其购买频率；\n",
    "\n",
    "- 对于重要发展用户，他们虽然最近没有购买，但以往购买频率高，可以做触达，以防止流失；\n",
    "\n",
    "- 对于重要挽留客户，他们最近没有购买，以往购买频率也不高，特别容易流失，所以应该赠送优惠券或推送活动信息，唤醒购买意愿。"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
